Extending extended logistic regression: Extended versus separate versus ordered versus censored

Jakob W. Messner*, Georg J. Mayr, Daniel S. Wilks, Achim Zeileis

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Extended logistic regression is a recent ensemble calibration method that extends logistic regression to provide full continuous probability distribution forecasts. It assumes conditional logistic distributions for the (transformed) predictand and fits these using selected predictand category probabilities. In this study extended logistic regression is compared to the closely related ordered and censored logistic regression models. Ordered logistic regression avoids the logistic distribution assumption but does not yield full probability distribution forecasts, whereas censored regression directly fits the full conditional predictive distributions. The performance of these and other ensemble postprocessing methods is tested on wind speed and precipitation data from several European locations and ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). Ordered logistic regression performed similarly to extended logistic regression for probability forecasts of discrete categories whereas full predictive distributions were better predicted by censored regression.

Original languageEnglish
JournalMonthly Weather Review
Volume142
Issue number8
Pages (from-to)3003-3014
Number of pages12
ISSN0027-0644
DOIs
Publication statusPublished - 2014
Externally publishedYes

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